{"id":"https://openalex.org/W2767313827","doi":"https://doi.org/10.4018/ijaeis.2018010104","title":"Reconstruction of Missing Hourly Precipitation Data to Increase Training Data Set for ANN","display_name":"Reconstruction of Missing Hourly Precipitation Data to Increase Training Data Set for ANN","publication_year":2017,"publication_date":"2017-11-08","ids":{"openalex":"https://openalex.org/W2767313827","doi":"https://doi.org/10.4018/ijaeis.2018010104","mag":"2767313827"},"language":"en","primary_location":{"id":"doi:10.4018/ijaeis.2018010104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijaeis.2018010104","pdf_url":null,"source":{"id":"https://openalex.org/S129247094","display_name":"International Journal of Agricultural and Environmental Information Systems","issn_l":"1947-3192","issn":["1947-3192","1947-3206"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Agricultural and Environmental Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035235948","display_name":"Hema Nagaraja","orcid":null},"institutions":[{"id":"https://openalex.org/I154970844","display_name":"Jaypee Institute of Information Technology","ror":"https://ror.org/05sttyy11","country_code":"IN","type":"education","lineage":["https://openalex.org/I154970844"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Hema Nagaraja","raw_affiliation_strings":["Department of Computer Science, Jaypee Institute of Information Technology, Noida, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Jaypee Institute of Information Technology, Noida, India","institution_ids":["https://openalex.org/I154970844"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008563545","display_name":"Krishna Kant","orcid":"https://orcid.org/0000-0001-5743-9944"},"institutions":[{"id":"https://openalex.org/I55016150","display_name":"Manav Rachna International Institute of Research and Studies","ror":"https://ror.org/02kf4r633","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405253735","https://openalex.org/I55016150"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Krishna Kant","raw_affiliation_strings":["Manav Rachna International University, Faridabad, India"],"affiliations":[{"raw_affiliation_string":"Manav Rachna International University, Faridabad, India","institution_ids":["https://openalex.org/I55016150"]}]},{"author_position":"last","author":{"id":null,"display_name":"K. Rajalakshmi","orcid":null},"institutions":[{"id":"https://openalex.org/I154970844","display_name":"Jaypee Institute of Information Technology","ror":"https://ror.org/05sttyy11","country_code":"IN","type":"education","lineage":["https://openalex.org/I154970844"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K. Rajalakshmi","raw_affiliation_strings":["Jaypee Institute of Information Technology, Noida, India"],"affiliations":[{"raw_affiliation_string":"Jaypee Institute of Information Technology, Noida, India","institution_ids":["https://openalex.org/I154970844"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5035235948"],"corresponding_institution_ids":["https://openalex.org/I154970844"],"apc_list":null,"apc_paid":null,"fwci":0.1539,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54203877,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9","issue":"1","first_page":"62","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.7717453837394714},{"id":"https://openalex.org/keywords/precipitation","display_name":"Precipitation","score":0.7428591251373291},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.6583396196365356},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6490943431854248},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5729395747184753},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.48503297567367554},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4720526337623596},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4435618221759796},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.43963927030563354},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.354697585105896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3408639430999756},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.30993103981018066},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.29592111706733704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2749572694301605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26194000244140625},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2294100821018219},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1463812291622162}],"concepts":[{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.7717453837394714},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.7428591251373291},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6583396196365356},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6490943431854248},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5729395747184753},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.48503297567367554},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4720526337623596},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4435618221759796},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43963927030563354},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.354697585105896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3408639430999756},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.30993103981018066},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.29592111706733704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2749572694301605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26194000244140625},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2294100821018219},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1463812291622162},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijaeis.2018010104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijaeis.2018010104","pdf_url":null,"source":{"id":"https://openalex.org/S129247094","display_name":"International Journal of Agricultural and Environmental Information Systems","issn_l":"1947-3192","issn":["1947-3192","1947-3206"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Agricultural and Environmental Information Systems","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jaeis0:v:9:y:2018:i:1:p:62-84","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAEIS.2018010104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1550206690","https://openalex.org/W1559455857","https://openalex.org/W1876878732","https://openalex.org/W1913009830","https://openalex.org/W1970551614","https://openalex.org/W1973659575","https://openalex.org/W1978524927","https://openalex.org/W1992311451","https://openalex.org/W2001398603","https://openalex.org/W2002016471","https://openalex.org/W2003200904","https://openalex.org/W2066430115","https://openalex.org/W2074770406","https://openalex.org/W2082864892","https://openalex.org/W2085353151","https://openalex.org/W2094312930","https://openalex.org/W2094653192","https://openalex.org/W2102148524","https://openalex.org/W2114824684","https://openalex.org/W2116386142","https://openalex.org/W2139873467","https://openalex.org/W2158638608","https://openalex.org/W2177044256","https://openalex.org/W4232119378","https://openalex.org/W4241561347"],"related_works":["https://openalex.org/W3216372614","https://openalex.org/W2187819724","https://openalex.org/W1510432176","https://openalex.org/W2786391746","https://openalex.org/W3132346564","https://openalex.org/W2991483587","https://openalex.org/W4220952526","https://openalex.org/W1493121153","https://openalex.org/W2914559142","https://openalex.org/W3164494351"],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,56,111,136],"hourly":[4],"precipitation":[5,25,58,61,113],"estimation":[6,133],"capacities":[7],"of":[8,33,76],"ANN":[9,126,137,146],"using":[10,16],"raw":[11,140,149],"data":[12,15,26,114,130,150,162],"and":[13,51,84,96,153],"reconstructed":[14,129,161],"proposed":[17,45],"Precipitation":[18],"Sliding":[19,80,87],"Window":[20,81,88],"Period":[21,82,89],"(PSWP)":[22],"method.":[23],"The":[24,44,121,142],"from":[27,38],"11":[28],"Automatic":[29],"Weather":[30],"Station":[31],"(AWS)":[32],"Delhi":[34],"has":[35,131],"been":[36],"obtained":[37],"Jan":[39],"2015":[40],"to":[41,54,109],"Feb":[42],"2016.":[43],"PSWP":[46],"method":[47],"uses":[48],"both":[49],"time":[50],"space":[52],"dimension":[53],"fill":[55,110],"missing":[57,112],"values.":[59],"Hourly":[60],"follows":[62],"patterns":[63,75,117],"in":[64,119],"particular":[65],"period":[66,102],"along":[67],"with":[68,128,139,148,160],"its":[69],"neighbor":[70],"stations.":[71],"Based":[72],"on":[73,116],"these":[74],"precipitation,":[77],"Local":[78],"Cluster":[79,86],"(LCSWP)":[83],"Global":[85],"(GCSWP)":[90],"are":[91],"defined":[92],"for":[93,145,156],"single":[94],"AWS":[95],"all":[97],"AWSs":[98],"respectively.":[99],"Further,":[100],"GCSWP":[101],"is":[103,151,163],"classified":[104],"into":[105],"four":[106],"different":[107],"categories":[108],"based":[115],"followed":[118],"it.":[120],"experimental":[122],"results":[123,134],"indicate":[124],"that":[125,155],"trained":[127,138,147,159],"better":[132],"than":[135],"data.":[141],"average":[143],"RMSE":[144],"0.44":[152],"while":[154],"neural":[157],"network":[158],"0.34.":[164]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
